activity
20242026
collaborators

9 papers

cs.CV2026

MDPBench: A Benchmark for Multilingual Document Parsing in Real-World Scenarios

Zhang Li, Zhibo Lin, Qiang Liu +7

We introduce Multilingual Document Parsing Benchmark, the first benchmark for multilingual digital and photographed document parsing. Document parsing has made remarkable strides,…

cs.CV2025

LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance

Zhang Li, Biao Yang, Qiang Liu +7

While large multi-modal models (LMMs) demonstrate promising capabilities in segmentation and comprehension, they still struggle with two limitations: inaccurate segmentation and ha…

cs.CV2025

MSTAR: Box-free Multi-query Scene Text Retrieval with Attention Recycling

Liang Yin, Xudong Xie, Zhang Li +2

Scene text retrieval has made significant progress with the assistance of accurate text localization. However, existing approaches typically require costly bounding box annotations…

cs.CV2025

MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm

Zhang Li, Yuliang Liu, Qiang Liu +8

We introduce MonkeyOCR, a document parsing model that advances the state of the art by leveraging a Structure-Recognition-Relation (SRR) triplet paradigm. This design simplifies wh…

cs.CV2025

VisuRiddles: Fine-grained Perception is a Primary Bottleneck for Multimodal Large Language Models in Abstract Visual Reasoning

Hao Yan, Xingchen Liu, Hao Wang +11

Recent strides in multimodal large language models (MLLMs) have significantly advanced their performance in many reasoning tasks. However, Abstract Visual Reasoning (AVR) remains a…

cs.CV2025

WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild?

An-Lan Wang, Jingqun Tang, Liao Lei +10

The rapid advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced capabilities in Document Understanding. However, prevailing benchmarks like DocVQA an…